Multimodality is a subfield of Natural Language Processing (NLP) that refers to the capability of a system or method to process input of different types or “modalities”, such as natural language text, speech, audio, images, video, and programming languages in NLP applications. It involves developing algorithms and models that can process and analyze information from multiple modalities, and integrate them to form a unified representation of the input.

Speech & Audio in NLP is a subfield of Multimodality that deals with the processing of spoken language and audio data in conjunction with textual data. It involves developing algorithms and models that can recognize and transcribe speech and audio, understand and interpret their meaning, and generate natural language responses.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Text Generation is a subfield of Natural Language Processing (NLP) which attempts to teach machines to automatically generate texts that are indistinguishable from texts written by humans.

Multilinguality is a subfield of Natural Language Processing (NLP) that refers to the ability of machines to understand, process, and generate natural language text in multiple languages. Multilinguality is concerned with addressing the challenges that arise due to the linguistic and structural variations that exist across different languages. This includes variations in word order, syntax, grammar, or vocabulary.

Automatic Speech Recognition (ASR), or Speech-to-Text (STT), is a technology that converts spoken language into written text. It is a field that focuses on the development of systems and algorithms capable of transcribing spoken language accurately. ASR systems are designed to understand and interpret human speech, allowing computers to process and analyze spoken words.

Machine Translation is a subfield of Multilinguality and Text Generation that deals with developing algorithms and models for automatically translating text or speech from one language to another. It involves analyzing the input text or speech, generating a representation of its meaning in the source language, and then producing a new text or speech in the target language that conveys the same meaning.

This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4 %. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.

Publication:

TED-LIUM: an Automatic Speech Recognition dedicated corpus

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